Access to accurate data is important to most businesses. As an example, financial institutions, such as banks, may rely on large amounts of data to conduct their business. Data can be used to verify the identity of a person opening an account, to identify fraud and other risks associated with financial transactions conducted against accounts, and to provide access to needed information, such as personal and contact information, for customers. The more accurate the data, the better a financial institution is able to perform its day-to-day business functions.
Financial institutions often have access, directly or indirectly, to many sources of data that may be usable for business functions. For example, a bank seeking to assess the risk associated with a transaction (such as a deposited check) may access a number of different data sources to confirm identity and other information provided with the transaction.
Data maintained by different sources may have varying degrees of accuracy, sometimes depending on how the data is collected and maintained. For example, data accuracy may suffer as a result a person introducing typographical errors when the data is entered. In some cases, data that is accurate at one point in time may become less inaccurate over time, such as when a person's name or address changes and the latest information is not updated at the data source. Further, some data sources solicit information from many people without emphasis on the data being accurate (such as at social networking site), and so the data associated with a given person at the data source may be accurate for some collected data but not as to other collected data.
As yet another example of data inaccuracy, perhaps involving a more serious risk, information maintained at a data source may be inaccurate because it is been provided by third party for a fraudulent purpose. Specifically, a person conducting a transaction or opening an account may provide false information to a bank in order to use the transaction or account for improper purposes.
Financial institutions often access multiple data sources (e.g., data other than that collected at the institution itself) to reduce risk, since, among other things, the greater the number of independent sources that have information consistent with data being provided by a person, the more likely it is that the provided data can be trusted. As just one example, if a customer provides several pieces of personal information (such as a name, address, date of birth and social security number) in order to establish the customer's identity when conducting business, and if several different data sources each confirm that all of the pieces of personal information are, in fact, associated with just one person, there is a higher likelihood that the provided data is trustworthy.
As a result, it can be advantageous to access as many data sources as possible when it is important to confirm or verify data. Unfortunately, since the trustworthiness of data may vary from data source to data source, using multiple sources of data (where some sources are trustworthy and others are less trustworthy) may lead to a less reliable risk determination. As a result, data sources that have some degree of perceived untrustworthiness may not be used at all for determining risk, even though there may be some useful information at those data sources.